import torch
from torch import nn

import d2l


class Inception(nn.Module):

    # c1 -- c4 是每条路径的输出通道数
    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
        super(Inception, self).__init__(**kwargs)
        # 路径1，单1x1卷积层
        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
        # 路径2，1x1卷积层后接3x3卷积层
        self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
        # 路径3，1x1卷积层后接5x5卷积层
        self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
        # 路径4，3x3最大汇聚层后街1x1卷积层
        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)

    def forward(self, X):
        p1 = torch.relu(self.p1_1(X))
        p2 = torch.relu(self.p2_2(torch.relu(self.p2_1(X))))
        p3 = torch.relu(self.p3_2(torch.relu(self.p3_1(X))))
        p4 = torch.relu(self.p4_2(torch.relu(self.p4_1(X))))
        return torch.cat((p1, p2, p3, p4), dim=1)


b1 = nn.Sequential(
    nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)

b2 = nn.Sequential(
    nn.Conv2d(64, 64, kernel_size=1),
    nn.ReLU(),
    nn.Conv2d(64, 192, kernel_size=3, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)

b3 = nn.Sequential(
    Inception(192, 64, (96, 128), (16, 32), 32),
    Inception(256, 128, (128, 192), (32, 96), 64),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)

b4 = nn.Sequential(
    Inception(480, 192, (96, 208), (16, 48), 64),
    Inception(512, 160, (112, 224), (24, 64), 64),
    Inception(512, 128, (128, 256), (24, 64), 64),
    Inception(512, 112, (144, 288), (32, 64), 64),
    Inception(528, 256, (160, 320), (32, 128), 128),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)

b5 = nn.Sequential(
    Inception(832, 256, (160, 320), (32, 128), 128),
    Inception(832, 384, (192, 384), (48, 128), 128),
    nn.AdaptiveAvgPool2d((1, 1)),
    nn.Flatten()
)

net = nn.Sequential(
    b1, b2, b3, b4, b5,
    nn.Linear(1024, 10)
)

X = torch.rand(size=(1, 1, 96, 96))
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__, 'output shape:\t', X.shape)

lr, num_epochs, batch_size = 0.1, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)

d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu(), async_train=True)

d2l.plt.show()
